‘Citizen scientists’ classify wildlife

It lays camouflaged deep in the savannah, awaiting some sign of movement so it can capture its prey — in a photograph.

Researchers from the University of Minnesota’s Lion Research Center carefully placed this and hundreds of other cameras in Tanzania’s Serengeti National Park to understand more about the area’s wildlife. Now they need help analyzing the millions of images they’ve collected.

The project is now part of Zooniverse, a platform where “citizen scientists” can help with research. Anyone can log on to the Snapshot Serengeti website and help researchers classify photos.

“We’re using the innate capability of the human brain in ways that are better than what computers can do right now,” said Zooniverse co-founder and University physics professor Lucy Fortson.

Zooniverse is a platform developed by Oxford University in 2007. Since its inception, more than 720,000 people have participated in 17 worldwide projects, where they did things like classify new planets and cancer cells.

University graduate student Ali Swanson has led the camera-trap research project for the last three years. She placed hundreds of heat- and motion-sensing cameras in the Serengeti to gather information on animal interaction when humans aren’t interfering.

The Serengeti cameras have taken more than 3 million pictures.

“A lot of those pictures are of grass,” she said, “but they still need to be looked at.”

With so many people participating in Zooniverse projects, about 100,000 photos were classified Tuesday, when the Serengeti project site launched.

The combination of the amount of data Swanson and her colleagues gathered and the public appeal of African wildlife made the project a good candidate for Zooniverse, Fortson said.

“The photographs are so rich,” she said. “The developers did such a fantastic job taking a really complex problem … and making it engaging.”

Classification

When citizen scientists log on to the website, they are greeted with a research photo to classify. Volunteers have 48 types of animals to choose from and can narrow these down based on color, horn type, tail, build andpattern.

Once they select a type of animal, participants can read a description and see photos of that species from different angles to be sure. Then they select how many are present and click “identify.”

Natalie Birch, a senior studying Latin and ancient Greek, has used Zooniverse before to help classify ancient Greek texts. She said it can be overwhelming at first because she isn’t always familiar with the research.

“There’s always the fear,” she said. “What if I pick the wrong letter?”

But because hundreds of thousands of people classify the same Zooniverse images, the chance of the animal being misclassified is unlikely.

If users don’t reach a consensus on an image, Swanson said the research team will classify it.

Engagement

Swanson said the team will also manage social media and answer questions on discussion boards — so it remains active in the research process.

“It’s a surprising amount of work,” she said. “But it’s very exciting to be able to expect these images to be identified pretty accurately in the near future.”

Birch said Zooniverse projects can be addictive. She said she’ll classify images for hours at a time because it makes the research fun.